Journal of Data Mining in Genomics & Proteomics

Journal of Data Mining in Genomics & Proteomics
Open Access

ISSN: 2153-0602

Mingqi Wu

Mingqi Wu
Shell Projects and Technology, Shell Technology,
Center Houston, TX

  • Research Article
    Model-Free Inference for ChIP-Seq Data
    Author(s): Mingqi Wu, Monique Rijnkels and Faming LiangMingqi Wu, Monique Rijnkels and Faming Liang

    Due to its higher resolution mapping and stronger ChIP enrichment signals, ChIP-seq tends to replace ChIP-chip technology in studying genome-wide protein-DNA interactions, while the massive digital ChIP-seq data present new challenges to statisticians. To date, most methods proposed in the literature for ChIP-seq data analysis are model based, however, finding a single model workable for all datasets is impossible, given the complexity of biological systems and variations generated in the sequencing process. In this paper, we present a model-free approach, the so-called MICS (Model-free Inference for ChIP-Seq), for ChIP-seq data analysis. MICS has a few advantages over the existing methods: Firstly, MICS avoids assumptions for the data distribution, and thus it maintains high power even when model assumptions for the data are violated. Secondly, MICS employs a simulation-based method .. Read More»
    DOI: 10.4172/2153-0602.1000153

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